1. Field
The present disclosure relates generally to identifying the pose of a mobile platform in an environment. More particularly, the present disclosure relates to a method and apparatus for forming and fusing data streams that each include a measure of uncertainty to generate a pose estimate for the mobile platform within the environment.
2. Background
In some situations, it may be desirable to have a mobile robot that can move freely within an environment much in the way a human would. Physical landmarks, such as paint, tape, or magnets, which may typically be used to help a mobile robot move within an environment, may constrain a mobile robot to only follow pre-defined routes. Further, installing these types of physical landmarks may be more time-consuming and expensive than desired. To move more freely within an environment, a mobile robot may need to perform localization, which includes identifying the pose of the mobile robot within the environment. As used herein, a “pose” includes a position, an orientation, or both with respect to a reference coordinate system.
A mobile robot may use an external sensor system to perform localization. However, in some cases, line of sight between a mobile robot and the external sensor system may be obstructed by other objects, robots, and/or persons within the manufacturing environment. As one example, in an aircraft manufacturing environment, line of sight may be lost when the mobile robot operates underneath a wing of the aircraft, inside the wing, in the vicinity of factory objects such as cranes or columns, and/or in restricted areas. Once line of sight is lost, the mobile robot may stop receiving pose updates and may need to halt operations until line of sight has been recovered. Without localization, the mobile robot may be unable to navigate through the environment as precisely as desired.
Further, in a dynamic environment, carts, planes, work stations, vehicles, equipment platforms, other types of devices, human operators, or some combination thereof may move. Consequently, a mobile robot may be unable to solely rely on its surroundings to move through this type of environment or an environment filled with clutter or not segmented or structured efficiently. Currently available mobile robots may be unable to operate with the levels of performance and efficiency desired or maneuver around human operators in a manner as safe as desired in these different types of environments.
Additionally, in some cases, the equipment or devices used for localization may be more expensive, larger, or heavier than desired. In certain situations, the processing required to perform the localization with a desired level of accuracy may be more time-consuming or require more processing resources than desired. Therefore, it would be desirable to have a method and apparatus that take into account at least some of the issues discussed above, as well as other possible issues.